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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Systematic examination of the PREVENT equations for cardiovascular disease risk
Vaishnavi Krishnan1,2, Xiaoning Huang1, Chiadi E Ndumele3
1Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Background:
The Predicting Risk of cardiovascular disease (CVD) EVENTs (PREVENT) equations accurately estimate CVD risk, but how different combinations of ages and risk factors translate into risk estimates, including combinations required to meet clinically meaningful risk thresholds, is not intuitive.
Methods:
We calculated sex-specific estimatesion of 10-year risk of total CVD (composite of atherosclerotic CVD and heart failure) for a hypothetical person between the ages of 30-79 years with the PREVENT-CVD base equations and varied individual or multiple risk factors simultaneously, with all other risk factor levels set at sex-specific, population-based average values. Next, we examined how specific age and risk factor combinations would exceed clinically-meaningful thresholds. Secondary analyses included the PREVENT-CVD equations with add-on predictors and the 30-year PREVENT-CVD equations.
Results:
Ten-year risk estimates for a hypothetical person with average risk factor levels ranged from 0.3 %-17.4 % for a female and 0.7 %-22.8 % for a male with the PREVENT-CVD base equations, exceeding the clinically-relevant risk threshold of ≥7.5 % at age 68 years if female and at age 63 years if male. Additionally, a hypothetical person with Stage 3 CKD and diabetes would exceed the 10-year risk threshold of ≥7.5 % with the PREVENT-CVD equations at age 43 years if female and at age 36 years if male. Similar patterns were observed with add-on predictor equations. With average risk factor levels, 30-year PREVENT-CVD risk estimates ranged from 2.5 %-20.5 % for a female and 4.8 %-26.5 % for a male.
Conclusions:
These results can support clinicians and patients in the interpretability of the PREVENT equations and can inform clinician-patient discussions on preventive efforts.
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